MrP Model Specification
\[
\begin{aligned}
y &\sim \mathrm{Binomial}(n, k, \theta)\\
\mathrm{logit}(\theta) &= \alpha + \beta_{1}\text{Female}_{i} + \beta_{2}\text{Citizen}_{i} + \upsilon_{j[i]}^{\mathrm{Ancestry}} + \upsilon_{l[i]}^{\mathrm{Education}} + \upsilon_{m[i]}^{\mathrm{Age}} +\\
&\quad \upsilon_{o[i]}^{\mathrm{Partisan}} + \upsilon_{p[i]}^{\mathrm{Linked~Fate}} + \upsilon_{q[i],p[i]}^{\mathrm{Female \times Linked~Fate}} + \upsilon_{q[i],j[i]}^{\mathrm{Female \times Ancestry}} +\\
&\quad \upsilon_{p[i],j[i]}^{\mathrm{Linked~Fate \times Ancestry}}\\
\text{where}\\
\upsilon_{j}^{\mathrm{Ancestry}} &\sim \mathcal{N}(\left[\gamma_{0} + \gamma_{1}\text{% College}_{j} + \gamma_{2}\text{Median Age}_{j} \right], ~\sigma^{\mathrm{Ancestry}}) \quad \text{for } j \in \{1, 2, \dots, J\}\\
\upsilon_{l}^{\mathrm{Education}} &\sim \mathcal{N}(0, ~\sigma^{\mathrm{Education}}) \quad \text{for } l \in \{1, 2, \dots, L\}\\
\upsilon_{m}^{\mathrm{Age}}&\sim \mathcal{N}(0, ~\sigma^{\mathrm{Age}}) \quad \text{for } m \in \{1, 2, \dots, M\}\\
\upsilon_{o}^{\mathrm{Partisan}} &\sim \mathcal{N}(0, ~\sigma^{\mathrm{Partisan}}) \quad \text{for } o \in \{1, 2, \dots, O\}\\
\upsilon_{p}^{\mathrm{Linked~Fate}} &\sim \mathcal{N}(0, ~\sigma^{\mathrm{Linked~Fate}})\quad \text{for } p \in \{1, 2, \dots, P\}\\
\upsilon_{q,p}^{\mathrm{Female \times Linked~Fate}} &\sim \mathcal{N}(0, ~\sigma^{\mathrm{Female \times Linked~Fate}}) \quad \text{for } q \in \{1, 2, \dots, Q\} \text{ and } p \in \{1, 2, \dots, P\}\\
\upsilon_{q,j}^{\mathrm{Female \times Ancestry}} &\sim \mathcal{N}(0, ~\sigma^{\mathrm{Female \times Ancestry}}) \quad \text{for } q \in \{1, 2\} \text{ and } j \in \{1, 2, \dots, J\}\\
\upsilon_{p,j}^{\mathrm{Linked~Fate \times Ancestry}}&\sim \mathcal{N}(0, ~\sigma^{\mathrm{Linked~Fate \times Ancestry}}) \quad \text{for } p \in \{1, 2, \dots, P\} \text{ and } j \in \{1, 2, \dots, J\}
\end{aligned}
\]
Post-Stratification Stage
Post-Stratified estimate by Linked Fate and Ancestry or Sex based on the 2019 American Community Survey 5-year Integrated Public Use Microdata Series data (Ruggles et al. 2022)
\[\begin{aligned}\theta^{\mathrm{MrP}} = \frac{\sum_{p,j \in P,J}N_{p,j}\cdot \theta_{p,j}}{\sum_{p,j \in P,J}N_{p,j}}\end{aligned}\]
\(\theta\) is the predicted probability of support for Trump from our model
\(N\) is the observed cell count in the post-stratification table
Summing over the population predictions within each pair of cells \(p,j \in P,J\) and dividing by the population total yields \(\theta^{\mathrm{MrP}}\) which represents the census-adjusted parameter estimate
Social identities in the Era of Trump
Race, ethnicity, gender, and social identity theory
What makes some identities more salient than others?
The Trump presidency and the salience of group identity
Theoretical Expectations
The salience of both pan-ethnic and gender-based dimensions of linked fate will vary across different sub-groups
The threat against multiple identities will activate intersectional linked fate
Relationship between intersectional linked fate and candidate preference is greater than the gender or pan-ethnic dimensions alone